The qualitative evaluations of the proposed framework is performed by observing the attention maps (obtained by the best performing incremental ResNet101 [1]} model) as shown in Figure REF . These attention maps are generated from the latent vectors (feature maps within the deeper layers of the network), which are then resized to the network input size. From Figure REF , we can observe that the ResNet101 [1]} (trained incremental using the proposed \(L_{IL}\)  loss function) focuses on recognizing the chest abnormalities while predicting the disease categories. For example, see the attention map in Figure REF  (R), where the network picked the consolidations compared to other scan regions. Similarly, see how the network paid attention to the opacities in Figure REF  (X). However, not all the focused areas (within attention maps) are clinically relevant. For example, see the focused areas in Figure REF  (L, T, and V). The incrementally trained network pays attention to these irrelevant areas because it could not differentiate between the lesions and the background regions due to their high spatial similarities within the candidate scan. However, it should also be noted that although these focused features are not clinically relevant, they do enable the classification model to predict the disease category (within each scan) accurately.
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